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An Atlas of Cyberspaces This is an atlas of maps and graphic representations of the geographies of the new electronic territories of the Internet, the World-Wide Web and other emerging Cyberspaces. These maps of Cyberspaces - cybermaps - help us visualise and comprehend the new digital landscapes beyond our computer screen, in the wires of the global communications networks and vast online information resources. The cybermaps, like maps of the real-world, help us navigate the new information landscapes, as well being objects of aesthetic interest. They have been created by 'cyber-explorers' of many different disciplines, and from all corners of the world. Some of the maps you will see in the Atlas of Cyberspaces will appear familiar, using the cartographic conventions of real-world maps, however, many of the maps are much more abstract representations of electronic spaces, using new metrics and grids. An Atlas of Cyberspaces
data visualization - Google Search
XML Extensible Markup Language (XML) is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. It is defined in the XML 1.0 Specification[3] produced by the W3C, and several other related specifications,[4] all free open standards.[5] The design goals of XML emphasize simplicity, generality, and usability over the Internet.[6] It is a textual data format with strong support via Unicode for the languages of the world. Although the design of XML focuses on documents, it is widely used for the representation of arbitrary data structures[7], for example in web services. Many application programming interfaces (APIs) have been developed to aid software developers with processing XML data, and several schema systems exist to aid in the definition of XML-based languages.

XML

XQuery XQuery XQuery is a query and functional programming language that is designed to query and transform collections of structured and unstructured data, usually in the form of XML, text and with vendor-specific extensions for other data formats (JSON, binary, etc.). XQuery 3.0 is currently being developed by the XML Query working group of the W3C and has reached Last Call Working Draft status.[3] XQuery 1.0 was developed by the XML Query working group of the W3C. The work was closely coordinated with the development of XSLT 2.0 by the XSL Working Group; the two groups shared responsibility for XPath 2.0, which is a subset of XQuery 1.0.
XPath, the XML Path Language, is a query language for selecting nodes from an XML document. In addition, XPath may be used to compute values (e.g., strings, numbers, or Boolean values) from the content of an XML document. XPath was defined by the World Wide Web Consortium (W3C).[1] Overview[edit] The XPath language is based on a tree representation of the XML document, and provides the ability to navigate around the tree, selecting nodes by a variety of criteria.[2][3] In popular use (though not in the official specification), an XPath expression is often referred to simply as "an XPath". XPath XPath
An XML schema is a description of a type of XML document, typically expressed in terms of constraints on the structure and content of documents of that type, above and beyond the basic syntactical constraints imposed by XML itself. These constraints are generally expressed using some combination of grammatical rules governing the order of elements, Boolean predicates that the content must satisfy, data types governing the content of elements and attributes, and more specialized rules such as uniqueness and referential integrity constraints. The mechanism for associating an XML document with a schema varies according to the schema language. The association may be achieved via markup within the XML document itself, or via some external means. Capitalization[edit] Validation[edit] XML schema XML schema
XML database An XML database is a data persistence software system that allows data to be stored in XML format. These data can then be queried, exported and serialized into the desired format. XML databases are usually associated with document-oriented databases. Two major classes of XML database exist:[1] XML-enabled: these may either map XML to traditional database structures (such as a relational database[2]), accepting XML as input and rendering XML as output, or more recently support native XML types within the traditional database. This term implies that the database processes the XML itself (as opposed to relying on middleware).Native XML (NXD): the internal model of such databases depends on XML and uses XML documents as the fundamental unit of storage, which are, however, not necessarily stored in the form of text files. XML database
XSLT XSLT XSLT (Extensible Stylesheet Language Transformations) is a language for transforming XML documents into other XML documents,[1] or other objects such as HTML for web pages, plain text or into XSL Formatting Objects which can then be converted to PDF, PostScript and PNG.[2] The original document is not changed; rather, a new document is created based on the content of an existing one.[3] Typically, input documents are XML files, but anything from which the processor can build an XQuery and XPath Data Model can be used, for example relational database tables, or geographical information systems.[1] XSLT is a Turing-complete language, meaning it can specify any computation that can be performed by a computer.[4][5]
Data architecture Data architecture In information technology, data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations.[1] Data is usually one of several architecture domains that form the pillars of an enterprise architecture or solution architecture.[2] Overview[edit] A data architecture should[neutrality is disputed] set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems.
Business intelligence Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle large amounts of unstructured data to help identify and develop new opportunities. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability.[1] Generally, Business Intelligence is made up of an increasing number of components, these are: Multidimensional aggregation and allocationDenormalization, tagging and standardizationRealtime reporting with analytical alertInterface with unstructured data sourceGroup consolidation, budgeting and rolling forecastStatistical inference and probabilistic simulationKey performance indicators optimizationVersion control and process managementOpen item management

Business intelligence

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Predictive analytics Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events.[1][2] In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. Predictive analytics is used in actuarial science,[3] marketing,[4] financial services,[5] insurance, telecommunications,[6] retail,[7] travel,[8] healthcare,[9] pharmaceuticals[10] and other fields. Predictive analytics